Adaptive control is a method to tune controller parameters automatically. Adaptive technology has been most successful in applications where the method can be standardized to tune one or a few parameters (e.g. in the automotive industry for fuel injection) or in difficult applications where advanced process engineers can justify spending time and resources to tune the adaptive controllers and develop “safety nets” so that they behave in robust and stable manner.
A class of algorithms for adaptive control was tried in the 1950s, but those methods did not enjoy as much success as expected because, in part, situations arose when the systems became unstable. Similar methods enjoyed a renaissance in the 1970s when inexpensive computers became available for process control and more complex algorithms could be applied in real time.
FIG. 1 illustrates one embodiment of a prior art adaptive control system 10 utilizing a process known as “certainty equivalence adaptive” (“CEA”) control. In general, a CEA control system 10 consists of four distinct elements: a state estimator 12, an adjustable model 14, a control design algorithm 16, and a controller 18. The CEA control system 10 controls a process 20. Differences exist between the model 14 and the actual process 20. These differences are reflected as “disturbances” entering the process and affecting the output. The control system 10 receives initial set-up parameters, either manually or from an expert system 22. Subsequent set-up parameters may also be provided as described below.
The state estimator 12 estimates the state of the process 20 to be controlled. The estimated state is passed to the adjustable model 14 and the controller 18. The model 14 estimates the output of the process 20 based on the estimated state, and the estimated output from the model 14 and the actual output of the process 20 are compared, and the difference is called the “model error”. The model error is used to adjust parameters in the model 14 in an attempt to reduce the model error. The new or adjusted parameters for the model 14 are passed to the control design algorithm 16 which utilizes an algorithm for control design which may be based on predictive control or pole-placement to adjust the parameters of the controller 18 in response to the new parameters of the model 14. Many different embodiments of linear and nonlinear CEA control have been published. See, for example, (Adaptive Control, Second Edition, K. J. Astrom and B. Wittenmark, Addison-Wesley Pub. Co. Reading Mass., 1995), which is incorporated herein by reference.
One problem with prior art adaptive control is that they are not robust with respect to model errors and disturbances due to interactions between the different adaptive components. One type of instability is illustrated in FIG. 2 and is known as “bursting”. This problem is also described in Golden, M. P. and Ydstie, B. E. “Ergodicity and Small Amplitude Chaos in Adaptive Control”, Automatica, Vol. 28, No. 1, January 1992, pp 11-25, which is incorporated herein by reference. FIG. 2 illustrates an adaptive controller used to control a system with a single output “y”, a single manipulated control input “u”, and a-single set point which is equal to zero. The output trends towards zero initially. But then there is a divergence before the output appears to converge again. This behavior continues and it causes spikes in the output. The problem is due to a nonlinear interaction among model adjustment, the control design and process itself which can be understood as follows. During periods of instability the data contains dynamic information and the controller is properly adapted. During stable periods there is little information present and the adaptive controller parameters drift until the loop becomes unstable. We then observe a burst and new data are accumulated that allows the model to retune. This cycle repeats. This type of instability is subtle since it cannot be solved using linear control theory. An article entitled Adaptive Control with Selective Memory, by Hill and Ydstie, published in International Journal of Adaptive Control and Signal Processing, 2004, 18:571-587, which is incorporated herein by reference, gives a description of the problem.
Many methods have been proposed to stop parameter drift and bursting. A brief review is given below and in Adaptive Control with Selective Memory, by Hill and Ydstie (cited above). Each of these requires the input of an extra system to check the stability of the closed loop as indicated in FIG. 1. The stability checking system can be implemented as an expert system 22 as suggested by (Adaptive Control, Second Edition, K. J. Astrom and B. Wittenmark, Addison-Wesley Pub. Co. Reading Mass., 1995), which is incorporated herein by reference. However, it has been found that it is difficult to develop a system with broad and general applicability. Good performance of adaptive control has been reported only for specific applications were significant engineering time was spent to adjust the adaptive controller to the particular application.
The deadzone approach was introduced to stop the estimation once error between the predictive control model and the process output gets below a certain value. The deadzone method utilizes a single predictive model but good performance and stability depend on correctly choosing the deadzone. It has to be larger than the noise level but not too large since this gives poor performance. It is therefore not easy to choose the deadzone in a practical application since real disturbances have non-stationary and unknown distribution functions.
The leakage method was introduced to prevent parameter drift by biasing the model parameters towards a set of fixed reference parameters. One disadvantage of the leakage method is that it does not prevent drift and burst unless the reference parameters are carefully chosen.
Another prior art solution is to periodically excite the input signal in an attempt to reduce parameter drift. This has several disadvantages. One disadvantage is that it may not be easy to choose the right amplitude and frequency of the excitation. Another disadvantage is that this method leads to reduced steady state performance because the signals are perturbed by the periodic excitation.
Adaptive Control with Selective Memory, by Hill and Ydstie (cited above), introduced a method to control drift and burst which is based on the selecting informative data. They used the Fisher Information Matrix and a variance estimator to decide if the data is informative. The parameter estimates are updated when the information matrix or the variance estimate increase. The selective memory approach is simple to implement and stability is independent of the noise characteristics and tunable parameters. A shortcoming of this method are that the estimator eventually becomes insensitive to new data and that bounds for the performance have not been established.
Several US patents have been issued that describe methods for adaptive process control. These include U.S. Pat. Nos. 5,282,261, 5,335,164, 5,568,378 and 6,577,908. The teachings in all of those patents fail to stop parameter drift and bursting.
Virtually all process industries that manufacture chemicals and petroleum related products, including many composite materials, food, metals, glass, and forest products, rely on the use of computer control to stabilize their processes and optimize their performance. More than 90% of all control loops for continuous processes in the process industries use the “PID” (proportional, integral, derivative) type controller. The theory for PID control and basic tuning principles were established more than 50 years ago. The technology is well established and well entrenched. New technology for controlling chemical processes has had very limited impact outside petroleum processing where multivariable predictive control has been applied. And even here the predictive controller is usually used in a supervisory mode to tune the set points of the PID controllers.
PID controllers have three parameters that need be tuned. About half of all PID controllers in industry are not well tuned (PID Controllers: Theory, Design and Tuning, K. Astrom and T Hagglund, 2nd edition Instrument Society of America, Research triangle Park, N.C., 1995). The manpower and expertise needed to maintain and tune these controllers is not available in many companies. This has resulted in significant losses due to mistuned controllers. Application of well tuned and optimized multivariable predictive controllers has led to total savings of in the excess of one billion dollars per year in the petrochemical industry (Morari, M. and G Gentilini, Challenges and opportunities in process control: Biomedical processes, AIChE J., Vol 47, pp 2140-2143, 2001). The fixed parameter predictive controller in current use is expensive to implement and maintain because it needs to be tuned by experts skilled in the art of process identification.
Many methods have been developed and methodologies exist to automatically tune and adapt control parameters using process data. In some applications it has been found that adaptive updating of parameters in model predictive control applications can give close to optimal control performance. Attempts to implement these very promising methods on a broad basis in industry have been fraught with difficulty, however. Hardware and software incompatibilities made it difficult and expensive to work across different computational platforms in the past. More importantly, the methods themselves have not been as robust as they should be. It is generally believed that the lack of robustness is due to parameter drift which sets in when the data used to update the model is not informative. Methods that were developed to solve this problem turned out to be more complex than expected. Many fixes are required to make an adaptive system work well under realistic operating conditions. As stated in Adaptive Control, Second Edition, K. J. Astrom and B. Wittenmark, Addison-Wesley Pub. Co. Reading Mass., 1995, “adaptive systems are not black box solutions that are a panacea.”
Current adaptive controllers can therefore be more difficult to tune than PID controllers and the tuning methods are often less transparent than those offered for classical PID control with feedforward. The method developed here solves these problems since the adaptation is robust and is based on informative data only and the parameters that need to be set can be related directly to closed loop performance parameters.
There exists a market for adaptive control methods. As mentioned above, there are literally millions of controllers in the process industries that are not optimally tuned. The performance losses can be substantial. The long term vision for adaptive control is to replace these methods, which are based on 1940s technology, with more efficient adaptive methods that not only will be less expensive to implement and maintain, they also will give considerably better performance.
Accordingly, there is a need for improved adaptive control systems, devices, and methods. In particular, there is a need for adaptive control systems, devices, and methods which can reduce or eliminate parameter drift and bursting. Those and other advantages of the present invention will be described in more detail hereinbelow.